基于改进的YOLOv5和UAV图像的树冠病和害虫识别.
Gaoyuan Zhao1,2, Yubin Lan2, Yali Zhang2
1College of Mechanical and Electrical Engineering, Lingnan Normal University, Zhanjiang 524048, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
这项研究引入了改进的YOLOv5深度学习模型,用于使用无人机图像识别米病和害虫. 改进后的模型达到95.6%的平均精度,为农业监测提供了显著的进步.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 遥感 遥感 遥感 遥感
背景情况:
- 传统的手动调查病和害虫是低效和主观的.
- 农业需要大规模,快速,准确的监测方法.
研究的目的:
- 开发一个改进的深度学习模型,用于准确识别树的疾病和害虫.
- 增强YOLOv5模型在农业应用中的功能.
主要方法:
- 使用无人机 (UAV) 来捕获大米树冠的高分辨率图像.
- 开发了一种改进的YOLOv5模型 (YOLOv5_DWMix),结合了深度可分离卷积,MixConv,注意力机制和优化损失功能.
- 采用图像增强来训练该模型识别四种常见的水疾病和害虫,解决复杂环境和小数据集的挑战.
主要成果:
- 改进的YOLOv5_DWMix模型在检测大米疾病和害虫方面获得了95.6%的平均精度.
- 与原来的YOLOv5模型相比,平均精度提高了4.8%.
- 该模型显示了增强的速度,特征提取和强度.
结论:
- YOLOv5_DWMix模型是识别大米疾病和害虫的有效和先进工具.
- 该方法为使用无人机技术和深度学习的大规模区域农业监测提供了坚实的基础.
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